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Manuel Dahmen
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2020 – today
- 2024
- [j13]Sonja H. M. Germscheid
, Benedikt Nilges
Optimal design of a local renewable electricity supply system for power-intensive production processes with demand response. Comput. Chem. Eng. 185: 108656 (2024) - [j12]Daniel Mayfrank
End-to-end reinforcement learning of Koopman models for economic nonlinear model predictive control. Comput. Chem. Eng. 190: 108824 (2024) - [j11]Danimir T. Doncevic, Alexander Mitsos
A Recursively Recurrent Neural Network (R2N2) Architecture for Learning Iterative Algorithms. SIAM J. Sci. Comput. 46(2): 719- (2024) - [i15]Daniel Mayfrank, Na-Young Ahn, Alexander Mitsos, Manuel Dahmen:
Task-optimal data-driven surrogate models for eNMPC via differentiable simulation and optimization. CoRR abs/2403.14425 (2024) - [i14]Mehmet Velioglu, Song Zhai, Sophia Rupprecht, Alexander Mitsos, Andreas Jupke, Manuel Dahmen:
Physics-Informed Neural Networks for Dynamic Process Operations with Limited Physical Knowledge and Data. CoRR abs/2406.01528 (2024) - 2023
- [j10]Jan G. Rittig
Graph neural networks for temperature-dependent activity coefficient prediction of solutes in ionic liquids. Comput. Chem. Eng. 171: 108153 (2023) - [j9]Florian Joseph Baader
Demand response for flat nonlinear MIMO processes using dynamic ramping constraints. Comput. Chem. Eng. 172: 108171 (2023) - [j8]Artur M. Schweidtmann
Physical pooling functions in graph neural networks for molecular property prediction. Comput. Chem. Eng. 172: 108202 (2023) - [j7]Sonja H. M. Germscheid
Demand response scheduling of copper production under short-term electricity price uncertainty. Comput. Chem. Eng. 178: 108394 (2023) - [d1]Artur M. Schweidtmann
Software for "Physical Pooling Functions in Graph Neural Networks for Molecular Property Prediction". Zenodo, 2023 - [i13]Daniel Mayfrank, Alexander Mitsos, Manuel Dahmen:
End-to-End Reinforcement Learning of Koopman Models for Economic Nonlinear Model Predictive Control. CoRR abs/2308.01674 (2023) - [i12]Hannes Hilger, Dirk Witthaut, Manuel Dahmen, Leonardo Rydin Gorjão, Julius Trebbien, Eike Cramer:
Multivariate Scenario Generation of Day-Ahead Electricity Prices using Normalizing Flows. CoRR abs/2311.14033 (2023) - 2022
- [j6]Eike Cramer
Validation Methods for Energy Time Series Scenarios From Deep Generative Models. IEEE Access 10: 8194-8207 (2022) - [j5]Marco Langiu
Simultaneous optimization of design and operation of an air-cooled geothermal ORC under consideration of multiple operating points. Comput. Chem. Eng. 161: 107745 (2022) - [j4]Eike Cramer
Normalizing flow-based day-ahead wind power scenario generation for profitable and reliable delivery commitments by wind farm operators. Comput. Chem. Eng. 166: 107923 (2022) - [j3]Yue Guo
Personalized Algorithm Generation: A Case Study in Learning ODE Integrators. SIAM J. Sci. Comput. 44(4): 1911- (2022) - [c1]Philipp Glücker
Incorporating AC Power Flow into the Multi-Energy System Optimization Framework COMANDO. OSMSES 2022: 1-6 - [i11]Eike Cramer, Felix Rauh, Alexander Mitsos, Raúl Tempone, Manuel Dahmen:
Nonlinear Isometric Manifold Learning for Injective Normalizing Flows. CoRR abs/2203.03934 (2022) - [i10]Eike Cramer, Leonard Paeleke, Alexander Mitsos, Manuel Dahmen:
Normalizing Flow-based Day-Ahead Wind Power Scenario Generation for Profitable and Reliable Delivery Commitments by Wind Farm Operators. CoRR abs/2204.02242 (2022) - [i9]Eike Cramer, Dirk Witthaut, Alexander Mitsos, Manuel Dahmen:
Multivariate Probabilistic Forecasting of Intraday Electricity Prices using Normalizing Flows. CoRR abs/2205.13826 (2022) - [i8]Jan G. Rittig, Martin Ritzert, Artur M. Schweidtmann
Graph Machine Learning for Design of High-Octane Fuels. CoRR abs/2206.00619 (2022) - [i7]Jan G. Rittig, Karim Ben Hicham, Artur M. Schweidtmann
Graph Neural Networks for Temperature-Dependent Activity Coefficient Prediction of Solutes in Ionic Liquids. CoRR abs/2206.11776 (2022) - [i6]Artur M. Schweidtmann
Physical Pooling Functions in Graph Neural Networks for Molecular Property Prediction. CoRR abs/2207.13779 (2022) - [i5]Jan G. Rittig
Graph neural networks for the prediction of molecular structure-property relationships. CoRR abs/2208.04852 (2022) - [i4]Danimir T. Doncevic, Alexander Mitsos, Yue Guo, Qianxiao Li, Felix Dietrich, Manuel Dahmen, Ioannis G. Kevrekidis:
A Recursively Recurrent Neural Network (R2N2) Architecture for Learning Iterative Algorithms. CoRR abs/2211.12386 (2022) - 2021
- [j2]Marco Langiu, David Yang Shu
COMANDO: A Next-Generation Open-Source Framework for Energy Systems Optimization. Comput. Chem. Eng. 152: 107366 (2021) - [i3]Eike Cramer
Principal Component Density Estimation for Scenario Generation Using Normalizing Flows. CoRR abs/2104.10410 (2021) - [i2]Yue Guo, Felix Dietrich, Tom Bertalan, Danimir T. Doncevic, Manuel Dahmen, Ioannis G. Kevrekidis, Qianxiao Li:
Personalized Algorithm Generation: A Case Study in Meta-Learning ODE Integrators. CoRR abs/2105.01303 (2021) - [i1]Eike Cramer
Validation Methods for Energy Time Series Scenarios from Deep Generative Models. CoRR abs/2110.14451 (2021) - 2020
- [j1]Andrea König
Integrated design of processes and products: Optimal renewable fuels. Comput. Chem. Eng. 134: 106712 (2020)
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